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In an energy‐limited world, biomass may be converted to energy products through pyrolysis. A byproduct of this process is biochar. A better understanding is needed of the sorption characteristics of biochars, which can influence the availability of plant essential nutrients and potential water contaminants such as phosphorus (P) in soil. Knowledge of P retention and release mechanisms when applying carbon‐rich amendments such as biochar to soil is needed. The objectives of this study were to quantify the P sorption and availability from biochars produced from the fast pyrolysis of corn stover (Zea mays L.), Ponderosa pine (Pinus ponderosa Lawson and C. Lawson) wood residue, and switchgrass (Panicum virgatum L.). We determined the impact of biochar application to soils with different chemical characteristics on P sorption and availability. Sorption of P by biochars and soil–biochar mixtures was studied by fitting the equilibrium solution and sorbed concentrations of P using Freundlich and Langmuir isotherms. Biochar produced from Ponderosa pine wood residue had very different chemical characteristics than corn stover and switchgrass. Corn stover biochar had the highest P sorption (in average 79% of the initial solution P concentration) followed by switchgrass biochar (in average 76%) and Ponderosa pine wood residue biochar (in average 31%). Ponderosa pine wood residue biochar had higher bicarbonate extractable (available) P (in average 43%) followed by switchgrass biochar (33% of sorbed P) and corn stover biochar (25% of sorbed P). The incorporation of biochars to acidic soil at 40 g/kg (4%) increased the equilibrium solution P concentration (reduced the sorption) and increased available sorbed P. In calcareous soil, application of alkaline biochars (corn stover and switchgrass biochars) significantly increased the sorption of P and decreased the availability of sorbed P. Biochar effects on soil P was aligned with their chemical composition and surface characteristics.

A gronomy J our n al • Volume 10 0 , I s sue 3 • 2 0 0 8 551 ABSTRACT To improve site-specifi c N recommendations a more complete understanding of the mechanisms responsible for synergistic relationships between N and water is needed. Th e objective of this research was to determine the infl uence of soil water regime on the ability of corn (Zea mays L.) to use N derived from fertilizer and soil. A randomized split-block experiment was conducted in 2002, 2003, and 2004. Soil at the site was a Brandt silty clay loam (fi ne-silty, mixed, superactive frigid Calcic Hapludoll). Blocks were split into moderate (natural rainfall) and high (natural + supplemental irrigation) water regimes. Nitrogen rates were 0, 56, 112, and 168 kg urea-N ha -1 that was surface applied. Water, soil N, and N fertilizer use effi ciencies were determined. Plant utilization of soil N was determined by mass balance in the unfertilized control plots and by using the δ 15 N approach in fertilized plots. Findings showed that: (i) plants responded to N and water simultaneously; (ii) N fertilizer increased water use effi ciency (170 kg vs. 223 kg grain cm -1 in 0 and 112 kg N ha -1 treatments, respectively); and (iii) water increased the ability of corn to use N derived from soil (67.7 and 61.6% effi cient in high and moderate water regimes, respectively, P = 0.002) and fertilizer (48 and 44% effi cient in high and moderate water regimes, respectively, P = 0.10). Higher N use effi ciency in the high water regime was attributed to two interrelated factors. First, total growth and evapotranspiration (ET) were higher in the high than the moderate water regime. Second, N transport to the root increased with water transpired. For precision farming, results indicate that: (i) the amount of N fertilizer needed to produce a kg of grain is related to the yield loss due to water stress; and (ii) the rate constant used in yield goal equations can be replaced with a variable.

Residue cover influences temperature and water gradients in the soil profile. Changes in the physical environment of the soil influence NH3 volatilization from urea‐containing fertilizers. Field and laboratory experiments were conducted to investigate the influence of residue‐cover‐induced changes in soil water and temperature on NH3 volatilization as impacted by urea treatment with a nitrification and urease inhibitor. Fertilizer treatments were urea, urea plus dicyandiamide (DCD), urea plus N‐(n‐butyl)thiophosphoric triamide (NBPT), and urea plus NBPT and DCD. Following fertilizer application, the soil was either left bare or covered with corn (Zea mays L.) residue. Every 3 h over a 4‐d period, water potential, soil temperature, CO2 production, and NH3 volatilization were measured. The influence of fertilizer treatments on soil pH was determined in a laboratory incubation experiment conducted over 8 d under controlled environmental conditions. Treatments were similar to the field experiment, with NH3 volatilization, pH, and CO2 production measured daily. The NH3‐volatilization rate in the field was highest 2 d after urea application at a time that corresponded with daily maximum soil temperature and decreasing soil water content. Residue cover reduced NH3 volatilization. Volatilization of NH3 as a result of urea application was not increased when urea was treated with DCD. Ammonia volatilization as a result of urea treatment with NBPT was reduced by 100 times over untreated urea. During an incubation experiment, soil pH increased from 6.5 to 7.2 in the urea‐NBPT, and from 6.5 to 9.0 in the urea and urea‐DCD treatments. Associated with the pH increase in the urea‐NBPT treatment was a reduction in CO2 production when compared with the untreated soil.

ABSTRACT

Interactions between water and N may impact remote-sensingbased N recommendations. The objectives of this study were to determine the influence of water and N stress on reflectance from a corn (Zea mays L.) crop, and to evaluate the impacts of implementing a remote-sensing-based model on N recommendations. A replicated N and water treatment factorial experiment was conducted in 2002, 2003, and 2004. Yield losses due to water (YLWS) and N (YLNS) stress were determined using the 13 C discrimination (D) approach. Reflectance data (400-1800 nm) collected at three growth stages (V8-V9, V11-VT, and R1-R2) were used to calculate six different remote sensing indices (normalized difference vegetation index [NDVI], green normalized vegetation index, normalized difference water index [NDWI], N reflectance index, and chorophyll green and red edge indices). At the V8-V9 growth stage, increasing the N rate from 0 to 112 kg N ha 21 decreased reflectance in the blue (485 nm), green (586 nm), and red (661 nm) bands. Nitrogen had an opposite effect in the near-infrared (NIR, 840 nm) band. At the V11-VT growth stage, reflectance in the blue, green, and red bands were lower in fertilized than unfertilized treatments. At the R1-R2 growth stage, YLWS was highly correlated (r 5 0.58, P 5 0.01) with red reflectance and NDVI (r 5 20.61, P 5 0.01), while YLNS was correlated with all of the indices except NDVI. A remote sensing model based on YLNS was more accurate at predicting N requirements than models based on yield or yield plus YLWS. These results were attributed to N and water having an additive effect on yield, and similar optimum N rates (100-120 kg N ha 21 ) for both moisture regimes.

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Optimizing N fertilizer use in maize production is critical for maximizing profi t and reducing N losses and associated negative environmental impacts. Th at an optimal solution is possible can be inferred from studies that have evaluated crop yield response and N losses across a wide range of N application rates. For example, Broadbent and Carlton (1978) found that NO 3 leaching from irrigated maize was small when the rate of applied N fertilizer did not exceed requirements for 90% of maximum grain yield. Similarly, in a meta-analysis of N 2 O emissions from arable crops, van Groenigen et al. (2010) concluded that yield-scaled emissions were constant until N fertilizer inputs exceeded N uptake by the aboveground biomass. Th e EONR is the N rate at which no further increase in net return occurs, and this point on the response curve occurs well below maximum yield levels at grain and N fertilizer prices typical of the past 40 yr (Dobermann et al., 2011).In practice, the EONR is diffi cult to predict before planting because the actual shape of the yield response to applied N varies fi eld to fi eld, and year to year due to in-season weather and crop management operations that infl uence the N supply-crop N demand balance. Th e EONR can be estimated by (i) the amount of N the crop obtains from the indigenous N supply (including N mineralization from organic matter, wet-dry deposition, and in irrigated systems, the NO 3 -N applied with irrigation), (ii) the shape of the N response function relating yield to the rate of N application, and (iii) prices for N fertilizer and maize grain. Th e shape of the yield response is determined by the yield potential when the crop is no longer limited by N (which defi nes the maximum attainable yield level), the agronomic fertilizer effi ciency (AE, Δyield/Δapplied N), which in turn is determined by the effi ciency of N uptake from the applied N (the recovery effi ciency, RE) and the effi ciency with which the acquired N is converted to grain yield (the physiological effi ciency, PE) (Novoa and Loomis, 1981).Despite the dynamic nature of the crop N response, extension programs in most U.S. Corn Belt states have established N fertilizer recommendations based on algorithms derived from regional fi eld tests that do not directly account for fertilizer N use effi ciency (Dobermann et al., 2006a). While such approaches can perform well in the region where they were developed, they may not be robust in other regions with diff erent soils, climate, and crop rotations. Given the limitations of regional calibration and the high degree of temporal and spatial variability in factors aff ecting crop response to applied N, new approaches that are responsive to this variability are under development.One approach is to apply N in response to conditions during the growing season, such as in-season adjustment of the N application rate in relation to leaf or canopy N status using sensor technologies (Kitchen et al., 2010;Olfs et al., 2005) or a chlorophyll meter (Scharf et al., 2006). In-season adj...